Applying Machine Learning Algorithm in Fall Detection Monitoring System

Khawandi, Shadi; Ballit, Abbass; Daya, Bassam

doi:10.1109/cicn.2013.59

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…The principles and methods of fall detection were investigated in the article in reference (Mubashir, Shao, & Seed, 2013), CONTACT Guilin Zhang zhangguilin@sdust.edu.cn which points out that the existing fall detection techniques can be divided into three categories. The first type of method is based on machine vision (Panahi & Ghods, 2018) (Khawandi, Ballit, & Daya, 2013), in which images are captured by using the Microsoft Kinect R camera and processed to extract features using a detection algorithm. In addition, the SVM classifier is used to distinguish fall from normal motion.…”

Section: Introductionmentioning

confidence: 99%

An SVM fall recognition algorithm based on a gravity acceleration sensor

Hou

Wang

Xiao

et al. 2018

Systems Science & Control Engineering

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To address the increasing health care needs for an ageing population, in this paper, a method of detecting human movements using smartphones is proposed to decrease the risk of accidents in the elderly. The method proposed in this paper uses a mobile phone that has an embedded acceleration sensor to record human motion information that are divided into daily activities (walking, running, going up stairs, going down stairs, and standing still) and falling down. In the process of data acquisition, motion noise contains some interference, and thus the median filter is employed to de-noise and smooth the motion data. Moreover, we extract representative multi-group features and analyse the features by principal component analysis and singular value decomposition to reduce dimensions. Through experimental comparisons with various classifiers, the support vector machine classifier is selected to classify the extracted features. The accuracy of fall detection reached 96.072%, which proved the accuracy of our proposed method.

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Section: Introductionmentioning

confidence: 99%

An SVM fall recognition algorithm based on a gravity acceleration sensor

Hou

Wang

Xiao

et al. 2018

Systems Science & Control Engineering

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“…Nowadays, some well-known machine learning algorithms such as k-nearest neighbor (kNN) 13 , support vector machines (SVM) 1 , decision tree 14 and artificial neural networks (ANNs) 3 have emerged as a promising method in fall-related researches 14,15 .…”

Section: List Of Figures Vmentioning

confidence: 99%

Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection

et al. 2020

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Chapter 1. Introduction 1 Chapter 2. Literature Review 5 Chapter 3. System Overview 8 Data Acquisition and Processing 9 Distributed Hierarchical Deep Learning Chapter 4. Experiment and Results Experimental Setup Result and Analysis Chapter 5. Discussion Why to use resources on the cloud server? What are the requirements about the incoming data during the training process? Why to use paired smart insole rather than only one smart insole? Chapter 6. Conclusion Chapter 7. Suggested Future Research References iv List of Tables 4.1 The main specs of the mobile device's hardware. 4.2 The comparison experimental results of using smart insoles and wearable sensors placed on SOIs for fall detection,W-waist, H-hand, T-thigh, I-smart insole, with k-nearest neighbor (knn), support vector machines (svm), decision tree (dt), discriminant (dc),Multi-Layer Perceptron (mlp) and convolutional neural network (cnn). 4.3 The best results through the six algorithm studies. The combination represents the combination of smart Insole, smartwatch and smartphone. 4.4 Performance of two different neural networks CNN and MLP under two different parameter exchange protocols. 4.5 Running time and Running Memory usage. 5.1 The confusion matrix using CNN with only one smart insole as inputs. CM-confusion matrix, Fa-Falling, SD-Sit Down, Wa-Walking, BP-Bend to Pick up, St-Standing, Be-Bending, Si-Sitting, Ly-Lying, Sq-Squatting and SP-Squat to pick up 5.2 The confusion matrix using CNN with two smart insole as inputs. v List of Figures 3.1 This figure shows the fall detection system flowchart. The sensor data collected from the smartwatch, smartphone and smart insoles. All the data are sent to the smartphone simultaneously. The smartphone will combine all three sensor data and perform deep learning through the system. During the training process, each smartphone transmits the intermediate output and label to the cloud server which will calculate corresponding gradient-outputs ∇ n j L(w, b; x, y) and release it back to the smartphone. After finishing the training process, the smartphone will deliver the accumulated gradients to the cloud server, and the cloud server will update the consensus model according to these gradients. The updated weights will distribute to one or more smartphones which send the requests. 3.2 Overview of DHNN architecture. (a) is the traditional DNN architecture. (b) introduces HNN architecture including a single smartphone and the cloud server. And (c) illustrates DHNN architecture in which the system further extends to multiple mobile deices. 4.1 (i), (ii), (iii), (iv), (v) representing the changes of acceleration, angular velocity, and orientation on smartwatch and smartphone, as well as the moving trend of pressure center of the smart insoles when presenting five different kinds of fall movements. Plot (vi), (vii), (viii), (ix), (x) showing the changes when presenting five different ADLs. vi 4.2 The distributed hierarchical deep learning architecture used in the system evaluation. The blocks in orange and blue represen...

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“…Therefore, comparing the performances of these diverse algorithms is fundamentally not feasible in an objective manner, unless benchmarking datasets and well-defi ned evaluation strategies are used. For example, Khawandi et al [ 237 ] proposed an algorithm of learning using a decision tree for fall detection based on the simultaneous input from a video camera and a heart rate monitor, which showed a low error rate of 1.55 % in average on test data after training. However, no defi nition of a falling event was revealed, and a description of the used dataset and the learning speed of the algorithm were missing.…”

Section: Requirements and Challenges Of Machine Learning Strategiesmentioning

confidence: 99%